将caffe中的data和label分别写在两个layer中

最新推荐文章于 2024-02-07 10:51:58 发布

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本文探讨了深度学习在图像处理领域的应用，包括图像分割、语义识别、AR特效等，展示了深度学习如何提高图像处理任务的效率和准确性。

layer {
name: "data"
type: "Data"
top: "data"
include {
phase: TRAIN
}
transform_param {
mean_value: 104.00699
mean_value: 116.66877
mean_value: 122.67892
}
data_param {
source: "skin/data/traindata_lmdb"
batch_size: 1
backend: LMDB
}
}
layer {
name: "label"
type: "Data"
top: "label"
include {
phase: TRAIN
}
data_param {
source: "skin/data/trainlabel_lmdb"
batch_size: 1
backend: LMDB
}
transform_param {
# feature scaling coefficient: this maps the [0, 255] MNIST data to [0, 1]
scale: 0.00390625
}
}
layer {
name: "data"
type: "Data"
top: "data"
include {
phase: TEST
}
transform_param {
mean_value: 104.00699
mean_value: 116.66877
mean_value: 122.67892
}
data_param {
source: "skin/data/valdata_lmdb"
batch_size: 1
backend: LMDB
}
}
layer {
name: "label"
type: "Data"
top: "label"
include {
phase: TEST
}
data_param {
source: "skin/data/vallabel_lmdb"
batch_size: 1
backend: LMDB
}
transform_param {
# feature scaling coefficient: this maps the [0, 255] MNIST data to [0, 1]
scale: 0.00390625
}
}